You will get AI features in your web application with Azure OpenAI and GPT models
Rising Talent
Rising Talent
Project details
I add AI to software that already exists - your app, your data, your users - using Azure OpenAI and the OpenAI API from a .NET back end.
Most AI demos never ship because nobody wires them into a real system. That is the part I do: authentication, rate limits, cost control, logging, fallbacks when the model misbehaves, and a UI your users understand.
What you get:
• GPT-powered features inside your existing app, not a separate toy
• RAG over your own documents and database with vector search
• Function calling so the model can trigger real actions in your system
• Prompt design, token and cost controls, and safety limits
• Streaming responses and a clean chat or assistant UI
• Full source code and a handover call
Stack: C#, ASP.NET Core, Azure OpenAI, Azure AI Search, Python where it fits, Angular or React on the front.
Typical work: document Q and A, support assistants, summarisation and extraction, classification, natural-language search over internal data.
Tell me what you want the AI to do and which app it lives in, and I will tell you which tier fits.
Most AI demos never ship because nobody wires them into a real system. That is the part I do: authentication, rate limits, cost control, logging, fallbacks when the model misbehaves, and a UI your users understand.
What you get:
• GPT-powered features inside your existing app, not a separate toy
• RAG over your own documents and database with vector search
• Function calling so the model can trigger real actions in your system
• Prompt design, token and cost controls, and safety limits
• Streaming responses and a clean chat or assistant UI
• Full source code and a handover call
Stack: C#, ASP.NET Core, Azure OpenAI, Azure AI Search, Python where it fits, Angular or React on the front.
Typical work: document Q and A, support assistants, summarisation and extraction, classification, natural-language search over internal data.
Tell me what you want the AI to do and which app it lives in, and I will tell you which tier fits.
Programming Languages
Python, ASP.NET, C#Coding Expertise
Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$120
|
Standard
$520
|
Advanced
$1,280
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 18 days |
Number of Revisions | 1 | 2 | 3 |
Number of Pages | 1 | 3 | 6 |
Design Customization | - | - | - |
Content Upload | - | - | - |
Responsive Design | - | - | - |
Source Code |
Frequently asked questions
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AA
Abdurahman A.
Oct 10, 2015
Sample simple solution project with WPF (C#), Prism, MVVM, EF
About Ivan
.NET Full Stack Developer | React | Angular | AI | .NET
Mykolaiv, Ukraine - 3:24 pm local time
Hi, I'm Ivan. I help SaaS and enterprise teams ship AI that survives production — Azure OpenAI, AI agents, MCP servers and RAG pipelines — built on the .NET and Azure systems I've engineered for over a decade. Most "AI features" fail because they're demos bolted onto a codebase nobody hardened. As an AI integration developer who is also a full stack developer, I build the AI layer and the platform underneath it: auth, data, retrieval, cost control, monitoring, and a deployment you can actually maintain.
🤖 What I do as an AI Integration Developer
✔ AI integration into existing apps — Azure OpenAI, GPT models, function calling, structured output
✔ AI agent development — multi-step agents that call your real APIs and databases
✔ MCP server development — expose your systems as tools to Claude, Copilot and other AI clients
✔ RAG pipelines — document ingestion, chunking, embeddings, vector search, grounded answers
✔ AI-powered document processing — import, export, extraction, classification, summarization
✔ Chatbots and copilots embedded in SaaS dashboards, admin panels and internal tools
✔ AI cost, latency and quality control — caching, model routing, evaluation, guardrails
☁️ What I do as an Azure developer
✔ Azure App Services, Azure Functions, Service Bus, Key Vault, Blob Storage
✔ Azure OpenAI deployment, private networking, managed identity and secrets hygiene
✔ Azure DevOps and GitHub Actions CI/CD, Docker containers, infrastructure as code
✔ Cloud migration and cost optimization — on-prem to Azure, monolith to microservices
⚙️ What I do as a .NET full stack developer
✔ C# / ASP.NET Core backends, REST APIs and microservices — Clean Architecture, DDD, CQRS
✔ React and Angular front ends, TypeScript, NgRx, RxJS — dashboards, admin panels, SPAs
✔ SQL Server, PostgreSQL and MongoDB — schema design, query tuning, migrations
✔ SaaS platforms, B2B products, CRM and ERP systems, WPF desktop applications
✔ Rescuing slow, brittle or half-finished .NET systems — re-architecture and modernization
📊 Experience in numbers
✔ 12+ years as a .NET developer and Azure developer, now focused on AI integration
✔ 5 industries shipped in production: FinTech, healthcare, banking, automotive, IoT
✔ 10 portfolio projects — enterprise platforms, SaaS products and internal tools
✔ Full product ownership on every one — architecture through production deployment
🧩 AI, Azure and .NET services
• AI integration and Azure OpenAI development
• AI agent and MCP server development
• RAG and knowledge-base chatbot development
• AI document import, export and processing automation
• Azure cloud architecture, migration and DevOps
• .NET backend and REST API development
• Full stack web application development (.NET + React / Angular)
• SaaS platform development (multi-tenant)
• Database design, tuning and migration
• Legacy system modernization (monolith → microservices)
• System architecture and technical consulting
🔄 How I work as an AI integration developer
Discovery and use-case scoping → data and system audit → architecture plan → AI prototype you can click → integration into your app → evaluation, guardrails and cost tuning → Azure deployment and CI/CD → handover with documentation → support and scaling
I send working software early and often, not status reports. You see a running prototype before we commit to the full build.
🛠️ Tech stack
C# · .NET 9 · ASP.NET Core · Entity Framework Core · Dapper · MediatR · SignalR · Python · Azure · Azure OpenAI · Azure Functions · Service Bus · Key Vault · MCP · RAG · Vector Search · Angular · React · TypeScript · NgRx · RxJS · Docker · Kubernetes · Terraform · GitHub Actions · Azure DevOps · SQL Server · PostgreSQL · MongoDB · WPF · DevExpress · Clean Architecture · DDD · CQRS
🔷 Why clients hire me as an AI integration developer
• I ship AI into real systems, not demos — integration, data and deployment are the hard part, and that's my background
• 12+ years as a .NET developer means the platform under your AI is production-grade
• One engineer for AI, backend, frontend and cloud — no hand-offs, no coordination tax
• Architecture-first: I'll tell you when AI is the wrong answer and a plain query would do
• Tested, documented, maintainable code you can hand to your own team
📩 If you're looking for an AI integration developer to add Azure OpenAI, an AI agent or an MCP server to your product — or a .NET full stack developer to modernize the platform it runs on — send me your project. I'll reply within 2-3 hours with a clear architecture plan and next steps.
Steps for completing your project
After purchasing the project, send requirements so Ivan can start the project.
Delivery time starts when Ivan receives requirements from you.
Ivan works on your project following the steps below.
Revisions may occur after the delivery date.
Use case and data review
We agree on what the AI must do, which data it may see, and what "good enough" looks like before any code.
Prompt and retrieval setup
I prepare the prompts, embeddings and vector search over your data, and measure the answers against real examples.